Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work. Analyses using dense molecular markers are greatly increasing information about the architecture of these traits, but while some genes of large effect are found, even many dozens of genes do not explain all the variation. Hence, new methods of prediction of merit in breeding programmes are again based on essentially numerical methods, but incorporating genomic information. Long-term selection responses are revealed in laboratory selection experiments, and prospects for continued genetic improvement are high. There is extensive genetic variation in natural populations, but better estimates of covariances among multiple traits and their relation to fitness are needed. Methods based on summary statistics and predictions rather than at the individual gene level seem likely to prevail for some time yet. The framework can also be used for the analysis of traits such as litter size that take a few discrete values, and of binary characters such as survival to adulthood that have a polygenic basis. The quantitative genetics approach has diverse applications: it is fundamental to an understanding of variation and covariation among relatives in natural and managed populations, of the dynamics of evolutionary change, and of methods for animal and plant improvement and alleviation of complex disease.On the premise that many genes and the environment act and interact to determine the trait, founders recognized that it would be difficult if not impossible to determine the action of individual trait genes. Statistical methods were invented by A), which is the expected performance of offspring, and heritability . In view of the assumed complexity of the underlying gene action, involving many loci with unknown effects and interactions, much quantitative genetic analysis has, unashamedly, been at a level of the ‘black box’.The models and summary quantities defined by Fisher and Wright have remained at the heart of the subject not least because they provide ways to make predictions of quantities such as the response to artificial and natural selection. Useful parameters include, for example, breeding value , many identified to genes, segregating for human height (see later); but these QTL, likely to be individually among the most important, contribute only about 5 per cent of the genetic variation. In view of its complexity, it therefore seems likely that the black box will remain cloudy for a while, even though fed information on, inter alia, myriads of genetic markers, levels of gene expressions and trait phenotypes. Statistical methodology which works and is continually developed to incorporate extensive marker and other new data seems likely to remain important for some time yet: better to work with the whole beast rather than try to assemble its parts from inadequate instructions.Genetica 136, 211–386), and in a Nature Insight series .I will address some of the background and some of these questions in this personal perspective, which is inevitably uneven in coverage and references, and reflects my interests, biases, knowledge and lacunae. It will focus particularly on animal improvement, an area which has both stimulated many developments in quantitative genetics, and is relevant to the welfare of man. Other recent perspectives and summaries from different viewpoints can be found in, for example, papers by 2.Let us review the standard assumptions in quantitative genetic analysis, address whether they stand up, and if not how much it matters.(a)VA, interactions of effects between alleles within loci  and among loci   of individuals, D defines dominance relationships and VE the environmental variance. For the epistatic terms, # denotes element-by-element multiplication, but applies only for unlinked loci. Many more terms may be included, such as maternal genetic effects, and genotype × environment interaction. The model has unlimited opportunities for complexity. This is a strength, in that it is all-accommodating, and a weakness, in that datasets may be adequate to allow partitioning into only very few components.In the model proposed by  VAD, …) . These p(b)The regression of offspring phenotype on that of parent for the same or different traits is usually assumed to be linear and, equivalently, so is the regression of response on selection differential. This important assumption holds under multivariate normality of phenotypic and genotypic values and thus the central limit theorem assuming multifactorial inheritance. Some traits, such as litter size or lifespan, are clearly not normally distributed, but adequate transformations can be invoked or departures ignored.(c)Response = h2 × selection differential. Selection changes gene frequencies and hence the genetic variance, so predictions of response in subsequent generations formally require knowing individual gene effects and frequencies. Fisher's ‘infinitesimal model’, formalized by Response to the first generation of selection can be predicted from the breeder's equation 3.(a)X and Z are design matrices, β is a vector of fixed effects (e.g. years), a is a vector of random effects  and e is a vector of random errors; and var(y) = ZAZ’VA+IVE where A is the additive relationship matrix (equation (2.1)). The model is general and flexible: it can incorporate, albeit with increasing computing needs, other covariance terms such as common environment among full sibs, repeat observations, maternal genetic effects  and multiple traits.Estimates of genetic parameters such as heritability are needed as a basis for description and prediction. Traditional methods such as analysis of variance or regression cannot cope adequately with unbalanced data and the complex pedigrees found outside the laboratory. They have been superseded by more sophisticated methods, often in the context of livestock data , which hIn retrospect, a surprisingly recent development has been in the modelling and analysis of longitudinal traits such as body weight which changes over time. The variances and covariances can be described directly by continuous covariance functions  or, equiThe generality of the animal model and the fact that most field data  are highly unbalanced have created a need for sophisticated and general analytical methods. These use restricted maximum likelihood (REML) or Bayesian principles, facilitated by the availability of specialized computer packages . Indeed, as genotyping costs fall there are increasing opportunities to expand pedigrees. While relatively simple objectives are to estimate genetic variances and covariances, a broader aim is to use data on breeding success to obtain estimates of the genetic parameters of fitness per se . The animal model can cope with selection and assortative mating, but only if the data on which decisions are based is included . Animal breeders encounter many such problems, but they are typically more serious for data from natural populations where datasets may be small, poorly structured and include multiple traits. Some traits associated with fitness, i.e. the selection ‘criterion’, may not be recorded, and some individuals may die or leave the population before recording. (b)within families from the regression of phenotypic similarity of sibs for a trait on the actual proportion of genome shared as determined by SNP identity, and is free of confounding by environmental differences between families or maternal genetic effects  chips provides a different method to estimate genetic variances. Pairs of full sibs share 50 per cent of alleles on average, but because linked genomic regions are transmitted, the actual proportion shared varies about expectation, with a s.d. of approximately 4 per cent for humans ((c)Prediction of breeding values is a fundamental component of modern breeding programmes, as those with the highest values should be selected. The major unifying development, Best Linear Unbiased Prediction (BLUP), is due to Henderson , 1984 anall information on all traits on which selection is practised is included in the data. Further, if any selection is practised, the infinitesimal model assumption is implicit (but often forgotten) in the use of the relationship matrix A to quantify variances and covariances across generations.BLUP is best in the sense of minimum variance among linear predictors, but only if population parameters are well estimated. It is unbiased in that, as more data are accumulated, the predicted breeding values approach the true values; and while it allows for selection, requires the important but often unachievable proviso that 4.Many major assumptions are made in the applications of quantitative genetics, but the issue is not the formal correctness of models used, rather the extent to which they work reasonably well. There is not space for a full review, but more discussion and examples are given elsewhere e.g. . We firsA and A#A matrices in equation (2.1)). These in turn may be confounded with other parameters, such as genetic maternal effects to explain why, say, a daughter-dam correlation exceeds twice that of half sibs in the absence of epistasis. Linkage disequilibrium (LD) is patently present, but that owing to close linkage is assumed absent in the infinitesimal model. The orthogonality assumptions in equation (2.1) may not hold, but how should that be tested? Hence, much of the evidence based on quantitative information is unsatisfactory in being so inconclusive, for example in failing to reject even the infinitesimal model as the following examples show.A major problem is to obtain data of adequate structure and quantity. For example, in the infinitesimal model all genetic variation is assumed to be additive. In random mating populations it is, however, usually impossible to estimate epistatic variances with any precision because the coefficients are very small and highly correlated with those of non-epistatic components . Indeed, this has been the big quantitative genetics industry of the last two decades. The basic methods are to use associations generated by linkage or LD between marker genes and the trait to locate QTL or to identify and locate mutations having a phenotypic effect and a molecular signal, such as transposable elements. Linkage studies  have beeThere is an extensive literature on the basic methodology of QTL mapping e.g.  and, for(a)Rather than attempt to review or even summarize the field, I shall just give some examples of the results from the use of different techniques, roughly in descending order of precision, that both provide information and generate questions.Drosophila melanogaster, a method permitting precise location, Drosophila have an excuse) and 34 per cent affected locomotor behaviour to a stimulus; and she noted that similar screens have found 22 per cent of insertions affecting abdominal and 23 per cent affecting sternopleural bristle number. Some have large effects, however. In view of the fact that such a high proportion of sites are targets, it is not surprising that there is extensive pleiotropy. Mackay also notes that many show epistatic effects. Similarly, for a range of behavioural traits in mice, in a study of over 200 gene knockout lines, 19 per cent showed abnormal open-field activity , and likely others were discovered by companies but not entered. These were from 83 publications and represented 109 different traits (but many have pleiotropic effect), representing a major effort and expenditure. The number of animals involved in each analysis are far smaller than in the association studies in humans, although data are used from segregation within individual sires who have progeny-tested sons with accurate estimates of breeding value. As only few of the QTL have been finely mapped, there is uncertainty about which of those mapped in different studies to similar genomic regions are the same or different genetic lesions, and how many are false-positives. In a few cases in livestock the actual genes, all having large effect, have been identified and sequenced. Some were already known as major genes, such as double muscling in cattle, for which the myostatin gene has been identified as causative, and others were initially discovered in mapping studies, for example DGAT, which influences milk composition of dairy cattle ∝[p(1 − p)]−1, under rare mutation drift balance (r2) with a rare QTL. The hypothesis that most of the missing variation is associated with extreme frequencies is not, however, supported by the schizophrenia study  tend to be highest for conformation traits and mature size, typically 50 per cent or more, and lowest for fitness-associated traits such as fertility (e.g. A = h × CV), is typically higher for fitness-associated than conformation traits Vm/VE, show a surprisingly narrow range over many traits and species, centred about 0.1 per cent . If few loci are assumed to affect the trait and typical estimates of the strength of selection are assumed, the predicted variance is much lower than that observed . But eveVG on population size, nor explain the constancy of trait means. Various aspects of the fit to the data are enhanced both by assuming that the mutants are (nearly) additive for the trait but  recessive for fitness, and worsened by assuming that there are substantial pleiotropic effects on other traits and overall fitness  heterozygote superiority, but none are clear winners. h2 are determined has been less studied and is even less well-understood than that of VG. Evolution of VE requires genetic variation of phenotype given genotype, for which there is strong evidence in Drosophila There has been extensive theoretical analysis and simulation to develop methods for using individual QTL in plant and livestock breeding programmes by marker-assisted introgression of a QTL from another population or by marker-assisted selection to increase frequency of a segregating gene in the population e.g. . ClearlyMuch effort has been expended on QTL detection and on theoretical analysis of how best to incorporate them in improvement programmes. We have much less information on actual effectiveness because much is within commercial companies and conventional selection on continuous traits has continued alongside. In two recent reviews on applications in plant breeding, (b)The availability of marker panels of thousands of SNPs does, in contrast, appear to be bringing in a real paradigm shift following the pioneering study of A (equation (2.1)) by the realized relationship matrix as assessed using high-density markers We see the striking changes that have been produced in quantitative traits by selection, for example among breeds of dogs in body weight and behaviour, and in the productivity of modern livestock and crops. Can we expect continued change?The Illinois maize selection for high and low content of oil in the kernel has been continued since 1896. The low lines have reached a plateau , but the upward lines have continued responding for 100 generations (i.e. years, et al. Results for a limited number of generations are shown for cattle in Ne times the response in each early generation (It is not surprising that such continued responses are found, as in many other experiments . If manyneration . The newneration , althougneration . Taken tNe and likely long-term progress (Modern breeding programmes inevitably involve a concentration of improvement in populations of limited size so that effective multi-trait recording can be undertaken and intense selection practised. There is a multiplication pyramid from nucleus populations in poultry and pigs, and in dairy cattle a concentration through use of sires through artificial insemination worldwide. Breeding programmes can be designed to optimize the trade-off between high selection intensity with the use of relatives' information to increase short-term gain and the decrease in 9 chickens are raised so, with a mutation rate of 1.8 × 10−9, there are over 50 mutants at each DNA site. The problem is not that there is no new variation, but to identify the useful new variants. Although it would be impossible to identify a mutant for a quantitative trait such as body weight in birds down the multiplication pyramid, it might be possible for a disease-resistant mutant.For cattle there is evidence that population sizes were large following domestication, of the order of tens of thousands or more, but those in some modern breeds are of the order of 100. Even so the levels of molecular genetic diversity within breeds are at least as great as in human populations (We can be optimistic about the prospects for future improvement, not least because the input of molecular and high-throughput technologies to livestock improvement has so far been tiny. Clearly, there are limits imposed by the laws of thermodynamics, but by simply increasing the rate of live-weight gain of a bird, the efficiency of feed use is increased and also, a new consideration, greenhouse gas emissions per unit product is reduced. There are undoubtedly challenges, for example in the availability of water and climate change influences more generally, but new opportunities will come from new technology. Some, for example genomic selection, are really just extensions of classical quantitative genetic methods of increasing accuracy of selection. Others, for example changing or inserting new genes, provide radical ways of introducing new variation, but only if the public accepts them. Although conserved animal germ plasm far behind the commercial norm may harbour useful variants, I expect their contribution to genetic improvement to be small.Drosophila, the effectiveness is illustrated by the results of many selection experiments (e.g. Similarly, the large amounts of genetic variation found in natural populations show that traits can be changed rapidly and substantially as a consequence of natural selection. With fitness defined as some simple measure, like bristle number in nts e.g. . There aif these coincide with fitness ‘objectives’, implies adaptive evolution is not possible. Although these analyses indicate there are, indeed, trajectories that cannot be followed, sampling errors alone can lead to such inferences. To understand and predict changes or lack thereof, we greatly need more reliable information on the genetic covariances among multiple traits and on fitness profiles on many environments, but this is a massive task. In the presence of a major change of environment where fitness profiles change, the risk to a species seems more likely to come from other species-filling niches or evolving more rapidly rather than from its total inability to adapt.The ability to evolve depends on the additive genetic covariance structure of all the relevant traits, and whether the relevant combination actually expresses genetic variation. Recent analyses on genetic covariance matrices typically find that many of their eigenvalues are zero, such that the corresponding eigenvectors indicate directions of no variance e.g. , ch. 30,9.Our level of understanding of many features of quantitative traits is quite rudimentary: what the genes do and how they interact, how their effects are distributed, the extent and magnitude of pleiotropic effects, the relations to overall fitness, and how and why is so much variation maintained? At this stage, however, we find that the many classical genes of small effect model explains many of the phenomena we observe and provides a basis for predictions of change. We can and are using the new information we get, however. But we should bear in mind that, as Darwin perceived, evolution succeeds through simple selection.


Drosophila melanogaster. Further, we also have identified mutations in genes involved in metabolic and neurogenic pathways that affect development time (heterochronic genes). However, we do not know whether these loci affect variation in developmental time in natural populations.Previously, we have shown there is clinal variation for egg-to-adult developmental time along geographic gradients in Drosophila melanogaster from an altitudinal cline, and measured egg-adult development time for each line. We found not only a large amount of genetic variation for developmental time, but also positive associations of the development time with thermal amplitude and altitude. We performed genetic complementation tests using substitution lines with the longest and shortest developmental times and heterochronic mutations. We identified segregating variation for neurogenic and metabolic genes that largely affected the duration of the larval stages but had no impact on the timing of metamorphosis.Here, we constructed second chromosome substitution lines from natural populations of invected, mastermind, cricklet and CG14591 may affect natural variation in development time and thermal evolution.Altitudinal clinal variation in developmental time for natural chromosome substitution lines provides a unique opportunity to dissect the response of heterochronic genes to environmental gradients. Ontogenetic stage-specific variation in  Understanding the ontogenetic trajectories of life-history traits is necessary to explain and predict constraints and variation in the evolution of characters. In this context, any variation in the state of individuals prior to reproduction will be preserved to some degree at the reproductive stage. In this sense, the time elapsed from the embryo to the reproductive phase, commonly known as developmental time (DT), is directly related to an individual's reproductive success D. melanogaster.In holometabolous insects like fruit flies, which occupy ephemeral habitats, the impact of DT on fitness is further exaggerated Drosophila melanogaster populations of Australia et al.Changes in the timing of developmental processes – heterochrony – could account for many evolutionary changes among populations and species. However, the first criterion for heterochonic evolution is the existence of natural variation for developmental time. Several studies have shown latitudinal clinal variation for DT in Hippo, Notch and insulin signaling pathwayIn(2L)t and In(2R)R, two cosmopolitan polymorphic inversions of the second chromosome, affect DT invected and mastermind are involved in the development of the nervous system cricklet is a gene encoding a carboxylesterase involved in the metabolism of the juvenile hormone CG14591 has no clear association with any biological process.DT is a quantitative trait determined by multiple segregating genes that are sensitive to temperature variation Drosophila brings an impressive toolkit for dissecting multiple interacting loci with individually small effects that affect quantitative developmental traits Genetic complementation testing is a potent tool to investigate the contribution of individual genes to natural variation in quantitative complex traits Drosophila melanogaster of western Argentina. We show that variation in DT among populations is not only correlated with altitude but also with certain features of the thermal regime. Clinal variation in DT over short geographic distances and its association with climatic variables suggests that among-population differentiation is the consequence of different selective pressures along the altitudinal gradient. Also we show that allelic variants at candidate heterochronic genes invected, mastermind, criclket and CG14591 contribute to altitudinal variation in DT with stage-specific effects.We report the results of a survey of variation in DT using second chromosome substitution lines derived from sampling localities that lay along latitudinal and altitudinal gradients from several populations of Drosophila melanogaster females collected in six localities along latitudinal and altitudinal gradients in Western Argentina (February 2004 and February 2005). The geographical location, latitude, longitude, altitude and climatic information for each population are given in Canton-S B strain by standard techniques using balancer chromosomes ]. A single w; Cy/+ 2; Sb/+ 3 male from the progeny of each cross was crossed to w; Cy/Sp; Canton-S B females. Next, w; Cy/+ 2; Sb/Canton-S B males were crossed to w; Cy/Sp; Canton-S B females (G3). Females and males of genotype w; Cy/+ 2; Sb/Canton-S B were intercrossed at G4, and the Cy and the Sb balancers were eliminated in the next generation (G5), obtaining an isogenic second chromosome substitution line with genotype w; +2; Canton S B. By means of this protocol we generated 50 second chromosome substitution lines isogenic for one wild derived chromosome in an otherwise isogenic background common to all lines. An average of 9 lines per population was obtained.Isofemale lines were founded by rearing the progeny of gravid omosomes . To consFor each substitution line, 300 pairs of sexually mature flies were placed in egg collecting chambers for 8 hours. Eggs were allowed to hatch and batches of 30 first-instar larvae were transferred to culture vials containing 10 ml of cornmeal-agar-molasses medium . Emerged flies from each vial were collected every 12 hours and sorted by sex. We estimated DT as the time elapsed since the transfer of first-instar larvae to the vials until adult emergence. All vials were kept in an incubator at 25±1°C, under a 12-h light∶ 12-h dark cycle and at 70% humidity.Forward stepwise multiple regression analyses were performed using geographic and climatic variables separately to test for clinal variation in developmental time. The geographic variables considered were altitude and latitude; and the climatic variables were minimal mean temperature, maximal mean temperature, mean temperature and thermal amplitude.invected, cricklet, CG14591 and mastermind to natural variation in DT among the most divergent second chromosome substitution lines. Thus, from our set of isogenic lines we selected those exhibiting the most extreme phenotypes for DT (the fast and slow developing lines). These lines were crossed individually with lines carrying P [GT1] element mutated alleles in one of the four candidate genes identified by Mensch et al. (2008) P [GT1] element-free insertion line with the same genetic background (Canton-S B). The genotypes of the F1 progeny of the crosses were m/+i and Canton-S B/+i, respectively, where m is a mutant allele in one of the candidate genes derived from a P [GT1] element insertion line and +i represents a wild derived allele of the candidate gene. We used P [GT1] insertion lines BG00846, BG01339, BG01672 and BG01902 that carry mutant alleles for invected, cricklet, CG14591 and mastermind, respectively Quantitative complementation tests were performed to examine the contribution of We evaluated variation in developmental time among genotypes by means of a four-way analysis of variance (ANOVA) according to the mixed model:Y = μ + L + S + G+ R (L ×S× G) + L × G + L × S + G × S + L × G × S + E,Canton-S B or m) and sex, respectively. R stands for the among replicate effect (random), and E is the error . The first criterion that must be met for quantitative failure of complementation is a significant Line by Genotype interaction. However, quantitative failure of complementation can be explained either as due to allelism or epistatic interactions Canton-S B background is not greater than variance among lines in the mutant background where L, G and S are the fixed cross-classified effects of line (second chromosome substitution line), genotype  and pupal developmental time (PDT), involving the duration of all events that occurred before and after pupation, respectively. All statistical tests were performed using the STATISTICA package .D. melanogaster second chromosome harbors genetic variation in natural populations for this fitness related trait  and Lavalle fast , whereas the lines that took more time to reach the adult stage were San Blas slow  and Uspallata slow .A large amount of variation in DT was found among natural substitution lines, indicating that ed trait . Among tF1,185 = 6.59; p = 0.01) and females . Indeed, DT substitution lines derived from highland and lowland sampling sites differed, on average, by 12 hours in both sexes.Regression of DT on geographic variables revealed a significant and positive association between developmental time and altitude   and noneIn order to quantify within population genetic variation, we performed an ANOVA for each sampling locality . In all mastermind, invected, cricklet and CG14591, that mapped in the second chromosome. Thus, natural genotypes that differed in DT by more than five days were used in four independent QCTs. Our results showed a significant line by genotype interaction in each QCT suggesting a failure of complementation in all four candidate genes  with a DT ratio close to zero, suggesting that this chromosome did not contribute to natural genetic variation for this gene. All in all, our results not only confirm that mastermind, invected, cricklet and CG14591 are quantitative candidate genes for developmental time expression, but also that these genes exhibit natural allelic variants that contribute to natural variation in DT.Genetic variation in candidate genes was revealed by the different responses of natural chromosomes when combined with mutant alleles or the functional each QCT . Most raCanton-S B backgrounds  and pupal (PDT) stages. For all candidate genes, we found that the L × G interaction was significant for LDT but not for PDT  et al.(1999) drosophilids as well Also interesting is that the line by sex interaction did not contribute to variation in DT in any of the localities, suggesting the lack of genetic variation for sexual dimorphism of DT. Interestingly, Mensch et al. (2008) Regression analyses of DT on geographic and climatic variables indicated that altitudinal clines and the association with thermal amplitude reported in Folguera P element insertion lines that we used to characterize heterochronic candidate genes invected, mastermind, cricklet and CG14591 have natural allelic variants that affect DT. To our knowledge this the first time that natural allelic variation is described for these genes as well as the  characterization of the genetic basis of DT variation in natural populations. Moreover, the similar variances of natural allelic variants when combined with mutant and Canton S B alleles, in all QCTs, suggest an additive effect of allelic variants of mastermind, cricklet, CG14591 and invected on DT. However, non additive effects due to interactions with other second chromosome loci affecting DT cannot be ruled out. Notice, for instance, that the genotype Lavalle/mutant, exhibited faster larval developmental time than the Uspallata/mutant genotype, when the mutant allele is contributed by the P-element insertion line for invected  has been the difficulty in mapping individual genes affecting natural genetic variation because differences in genetic background can profoundly affect complex phenotypes invected . On the  CG14591 . These rinvected and mastermind are expressed early in development and also during the larval and pupal stages engrailed, is a transcription factor that is expressed in neuroectodermic cells mastermind is a key component of the Notch signaling pathway, which determines cell fate and regulates pattern formation. Likewise, cricklet  encodes a carboxylesterase that has been proposed to be associated with Juvenile hormone (JH) functions CG14591. Our study provides, therefore, the first record concerning functional significance of CG14591 in D. melanogaster.Noteworthy, two of the genes, invected alleles that not only affected larval developmental time but also influenced various adult body size traits As a complex trait, DT displays considerable genetic correlations with other life history and morphological characters. Particularly, it has been suggested that DT may be involved in a trade-off with body size, since attaining a large size may imply a longer feeding period, and thus a longer DT. Thus, variation in growth patterns, growth rate and the duration of the growth period, affect the age and size at maturity, implying that the study of juvenile growth may be crucial to understand life history evolution Drosophilaet al., unpublished results). Noteworthy, mastermind, cricklet and CG14591 were also shown to be candidates for the plastic responses of DT to temperature variation D. melanogaster and allied species The recognition that a single trait, DT, may be subdivided in “subtraits” that are not under the control of the same genes imply that the duration of larval and pupal stages responded to different selective pressures; a matter that may have broad implications in evolutionary genetics, since responses to selection may crucially depend on the genetic variance/covariance of individual “sub-traits”


Genetics contributes importantly to learning abilities and disabilities—not just to reading, the target of most genetic research, but also to mathematics and other academic areas as well. One of the most important recent findings from quantitative genetic research such as twin studies is that the same set of genes is largely responsible for genetic influence across these domains. We call these “generalist genes” to highlight their pervasive influence. In other words, most genes found to be associated with a particular learning ability or disability (such as reading) will also be associated with other learning abilities and disabilities (such as mathematics). Moreover, some generalist genes for learning abilities and disabilities are even more general in their effect, encompassing other cognitive abilities such as memory and spatial ability. When these generalist genes are identified, they will greatly accelerate research on general mechanisms at all levels of analysis from genes to brain to behavior. Genetic research has moved beyond merely demonstrating the importance of genetic influence. Most notably, intense research efforts are focused on attempts to identify the DNA responsible for this genetic influence, especially for reading disability, and we mention this work briefly. Nonetheless, quantitative genetic research such as twin studies that compare identical and fraternal twins continues to be important in charting the course for molecular genetic explorations. The most important example is multivariate genetic analysis, which makes it possible to investigate genetic links between variables rather than focusing on one variable at a time. The major goal of this article is to provide an overview of multivariate genetic research on learning abilities and disabilities, which consistently points to “generalist genes” that have pervasive effects. We also consider the implications of generalist genes for education and for cognitive neuroscience. In order to focus on this topic of generalist genes, we need to forgo presenting other topics important to the field of mind, brain, and education, such as the developmental interface between genes and environment . HoweverMore than 90% of teachers and parents say that they believe genetics to be at least as important as the environment for learning abilities and disabilities . In the Two decades of research make it clear that genetics is a surprisingly large part of the answer to why children differ in their ability to learn in school. Most research uses the twin method that compares resemblance for genetically identical twins  and for twins who are only 50% similar genetically . Genetic influence is suggested to the extent that MZ twins are more similar than DZ twins, reflecting the twofold greater genetic similarity of MZ as compared with DZ twins. For example, a review of twin studies of language disability reported concordance (the likelihood that one twin will be affected if the other twin is affected) of 75% for MZ twins and 43% for DZ twins . For reaMoreover, genetics is not only involved in disability. Twin studies also consistently point to substantial genetic influence for learning abilities throughout the normal distribution of individual differences. A review of twin studies that reported results for both learning disabilities and abilities found that the average weighted heritability (proportion of phenotypic variance that is attributed to genetic variance) was 0.43 for language disabilities and 0.25 for language abilities; 0.52 and 0.63 for reading disabilities and abilities, respectively; and 0.61 and 0.63 for mathematics disabilities and abilities, respectively .The case for substantial genetic influence on learning disabilities is so clear that most genetic research, especially in the area of reading disabilities, now focuses on using molecular genetics to identify the specific genes responsible for this genetic influence. Although progress has been slow, recent developments in molecular genetics are promising ;. For reUnivariate genetic analysis uses methods such as the twin method to estimate genetic and environmental contributions to individual differences (variance) on a single variable (univariate). If MZ twins are more similar than DZ twins for a trait, this suggests that genetic differences account for some of the observed (phenotypic) differences on the trait. Heritability estimates the extent to which genetic variance accounts for phenotypic variance. In contrast, multivariate genetic analysis focuses on the covariance (correlation) between two traits (bivariate) or multiple traits (multivariate) and uses the twin method to estimate genetic and environmental contributions to their covariance as well as the variance of each trait. In other words, multivariate genetic analysis estimates the extent to which genetic and environmental factors that affect one trait also affect another trait.The gist of multivariate genetic analysis lies in cross-trait twin correlations. Just as univariate genetic analysis compares MZ and DZ correlations for a single trait, multivariate genetic analysis compares MZ and DZ correlations across traits, called cross-trait twin correlations. To the extent that MZ cross-trait correlations are greater than DZ cross-trait correlations, this suggests that genetic differences mediate the phenotypic correlation between the traits.In practice, multivariate genetic analysis is conducted using structural equation model-fitting techniques based on the model shown in rA), which represents the extent to which genetic effects on trait X correlate with genetic effects on trait Y independent of the heritability of the two traits. A genetic correlation of 0.0 indicates that completely different genes affect the traits and a genetic correlation of 1.0 signifies that the same genes affect both traits. In other words, the genetic correlation is the probability that a gene found to be associated with one trait will also be associated with the other trait. An important feature of the genetic correlation is that it is independent of the heritability of the traits. That is, the genetic correlation can be high even if the heritabilities of the two traits are low and vice versa. In the interest of streamlining this article, we will not discuss a second multivariate genetic concept, the genetic contribution to the phenotypic correlation, which is represented in the model as the product of the genetic paths connecting trait X and trait Y . We will also not discuss the C and E correlations even though they also tell an interesting story  are substantial but somewhat lower than the genetic correlations among learning abilities  and NC ratings for English, mathematics, and science, we conducted separate Cholesky analyses at 7, 9, and 10 years. The genetic results are presented in 1 latent variable extracts genetic variance that is in common between g and academic performance in English and mathematics. The heritability of g at 7 years is shown as 0.37. The A1 loadings of 0.23 for English and 0.19 for mathematics indicate that a significant and substantial amount of the genetic variance on English and mathematics is shared in common with g. However, English and mathematics are more highly heritable than g: The heritability estimates from g (0.23/0.65 = 0.35). Similarly, only a third of the genetic variance on mathematics is shared in common with g (0.19/.065 = 0.29).Using TEDS data on g. This analysis is captured by the A2 latent variable. The significant and substantial loadings of English and mathematics on the A2 latent variable indicate that English and mathematics share genetic variance independent of g. For mathematics, about a third of its genetic variance is shared with English independent of g (0.21/0.65 = 0.32). The A3 latent variable indexes genetic variance that is unique to mathematics, that is, not shared with either g or English. Focusing on mathematics, the results suggest that about a third of its genetic variance is in common with both g and English, about a third is in common with English independent of g, and the remaining third is unique to mathematics. A similar conclusion would be reached for English if it were the last variable in the Cholesky analysis.An important feature of the Cholesky model is that it estimates genetic variance shared by English and mathematics that is independent of g, English, and mathematics (0.15/0.61 = 0.25); 34% is independent of g but in common with English and mathematics (0.21/0.61 = 0.34); 11% is independent of g and English but in common with mathematics; and 30% is unique to science.Similar results were obtained at 9 and 10 years, as shown in the second and third panels of g, English, and mathematics; 17% is independent of g but in common with English and mathematics; 6% is independent of g and English but in common with mathematics; and 29% is unique to science. This suggests that science at 10 years may have more to do with g genetically. However, the results for English and mathematics are similar at 10 years in suggesting that only about a third of their genetic variance is shared in common with g.At 10 years  genes found to be associated with a particular learning ability or disability (such as reading) will also be associated with other learning abilities and disabilities (such as mathematics). In addition, most  of these generalist genes for learning abilities (such as reading and mathematics) will also be associated with other cognitive abilities .Although finding generalist genes associated with learning disabilities is unlikely to have much direct impact on teachers in the classroom confronted with a particular child with a learning disability, such findings will have far-reaching ramifications in terms of educational research and, more practically, in terms of diagnosis, treatment, and prevention. At the most general level, identifying generalist and specialist genes will increase acceptance of genetic influence in education because DNA provides evidence for genetic influence that is much more direct than the evidence provided by quantitative genetic research such as twin studies.In terms of research, few educational researchers are likely to become involved in the quest to find genes associated with learning abilities and disabilities, but when the genes are found, they will be widely used in research as DNA risk indicators in much the same way that demographic risk indicators are currently used . MoreoveThe most immediate implication for education is the realization that genetic diagnoses of learning disabilities differ from traditional diagnoses, which are based on symptoms rather than causes. From a genetic perspective, learning disabilities are not distinct diagnostic entities: the same set of generalist genes affects learning abilities and disabilities. Finding generalist genes associated with learning disabilities will lead to new diagnostic classifications that are based on etiology rather than symptomatology.In terms of treatment, genes will be used clinically or educationally to the extent that response to treatment depends on genetic risk. This goal is part of a “personalized medicine” movement toward individually tailored treatments rather than treatments that are “one size fits all” .The most important benefit of identifying genes that put children at risk for developing learning disabilities is that the causal nature of genes means that they can serve as an early-warning system. This should facilitate research on interventions that prevent learning disabilities, rather than waiting until problems are so severe that they can no longer be ignored. The goal of early intervention fits with a general trend toward preventative medicine. Because vulnerability to learning disabilities involves many genes of small effect, genetic engineering is unimaginable for learning disabilities; interventions will rely on environmental engineering, primarily educational interventions.Acceptance of generalist genes will change the way we think about the brain and mind. In this final section, we discuss two genetic concepts—pleiotropy and polygenicity—that provide a foundation for understanding the effects of generalist genes on the brain and mind.Pleiotropy means that a gene has multiple effects. In terms of individual differences—which is the focus of genetic studies on learning abilities and disabilities—polymorphisms in these genes will also have pleiotropic effects. Pleiotropy is common in complex organisms and can be expressed at various biological levels, from a gene that mediates several intracellular signal transduction pathways to a gene that is expressed in different tissues . As one A powerful new tool for seeing pleiotropy in the brain is gene expression mapping. Gene expression can be indexed by the presence of RNA that is transcribed from DNA. A critical development in gene-expression mapping throughout the brain is the microarray that can detect the expression of all the genes in the genome simultaneously , which iO-methyltransferase (COMT) and brain-derived neurotrophic factor (BDNF)   . COMT islas.org) .www.genes2cognition.org). The generalist genes hypothesis predicts that functional genetic neuroimaging will also show general effects of allelic variation across brain regions and across tasks.Similar to early neuroimaging research, genetic neuroimaging work has focused on structural localization at the normative level of analysis, for example, in the transcriptome-mapping project in humans . StructuThe effects of pleiotropy (each gene affects multiple traits) are amplified by polygenicity (each trait is affected by multiple genes). Again, much of the discussion of pleiotropy is at a normal level rather than the individual-differences level, which is the critical level of analysis for learning abilities and disabilities. For common disorders and complex traits, genetic research on individual differences has undergone a revolution that has radically altered molecular genetic strategies. Instead of thinking about rare genetic disorders caused by a single-gene mutation of the sort that Mendel investigated in the pea plant, it is now generally accepted that common disorders are caused by many genes (polygenicity), which implies that each of these genes will have only a small effect. Single-gene disorders are usually extremely rare, with a frequency of one in tens of thousands, whereas the frequency of learning disabilities such as reading and mathematics disability is often considered to be as great as 5%. It is likely that a few cases of learning disability are due to single-gene disorders that contribute little to normal variation in learning ability. However, most researchers now believe that common disorders are caused by common genetic variants—the common disorder/common variant hypothesis —rather tPolygenicity is thought to be the reason why progress has been so slow in identifying genes associated with common disabilities and complex traits—very large samples are needed to attain the statistical power needed to detect very small effect sizes . In the In our opinion, pleiotropy and polygenicity make it likely that generalist genes result in “generalist brains” at the individual differences level of analysis. That is, polymorphisms in genetic input into brain structure and function have general effects, not modular effects . In othePleiotropy and polygenicity will make it difficult to investigate links between genes, brain, and behavior. Despite these challenges and complexities, when generalist genes are identified, they will provide three opportunities for empirical research on brain pathways between genes and learning abilities and disabilities. First, these genes will be identified on the basis of their prediction of learning disability. In other words, no matter how complex the brain pathways are between genes and learning abilities and disabilities, these genes will be anchored to a functional effect at the level of individual differences in learning abilities and disabilities. Second, each of these generalist genes will provide a window through which we can view brain mechanisms that are functionally related to learning disabilities . Third,


Understanding the genetic architecture of ecologically relevant adaptive traits requires the contribution of developmental and evolutionary biology. The time to reach the age of reproduction is a complex life history trait commonly known as developmental time. In particular, in holometabolous insects that occupy ephemeral habitats, like fruit flies, the impact of developmental time on fitness is further exaggerated. The present work is one of the first systematic studies of the genetic basis of developmental time, in which we also evaluate the impact of environmental variation on the expression of the trait.P[GT1]-element insertion lines of Drosophila melanogaster to identify novel genes affecting developmental time in flies reared at 25°C. Sixty percent of the lines showed a heterochronic phenotype, suggesting that a large number of genes affect this trait. Mutant lines for the genes Merlin and Karl showed the most extreme phenotypes exhibiting a developmental time reduction and increase, respectively, of over 2 days and 4 days relative to the control (a co-isogenic P-element insertion free line). In addition, a subset of 42 lines selected at random from the initial set of 179 lines was screened at 17°C. Interestingly, the gene-by-environment interaction accounted for 52% of total phenotypic variance. Plastic reaction norms were found for a large number of developmental time candidate genes.We analyzed 179 co-isogenic single Drosophila. At the same time, we also show that many heterochronic phenotypes may arise from changes in genes involved in several developmental mechanisms that do not explicitly control the timing of specific events. We also demonstrate that many developmental time genes have pleiotropic effects on several adult traits and that the action of most of them is sensitive to temperature during development. Taken together, our results stress the need to take into account the effect of environmental variation and the dynamics of gene interactions on the genetic architecture of this complex life-history trait.We identified components of several integrated time-dependent pathways affecting egg-to-adult developmental time in  Drosophila species occupy ephemeral habitats, such as rotting fruits that may result in selection for rapid development. Quoting Gould's Ontogeny and Phylogeny: "The timing of maturation is a primary variable in setting life history strategies. We have a prima facie case for ascribing direct significance to the change in developmental timing itself, not only to its morphological consequences" insertion lines can not be explain as a consequence of unfit flies. The most extreme phenotypes were exhibited by lines BG01543 and BG01412. The former developed 60 and 50 hours  faster and the latter 119 and 146 hours  slower than the control. We observed a significant line effect indicating mutational variance among single P-element insertion lines  differed significantly from the control. Mean values of all significant P[GT1] insertion lines tested at 25°C are shown in Additional file P-element caused an increase in DT, while in the rest of the significant lines DT was shortened as compared to the control (20%) , a gene encoding a glutamine-rich nuclear protein  insertion in exon 1 caused a reduction in the RNA expression levels in embryos, third instar larvae and adults but not in the pupal stage compared to the control  insertions. We also estimated the relative contribution of all random sources of variation  to the total variance.Analysis of variance (ANOVA) was utilized to assess the magnitude of mutational variance for DT at each temperature. In order to include all lines tested in different batches, individual DT scores were expressed as deviations from the mean of their contemporaneous co-isogenic controls, separately for males and females. Three-way ANOVA was computed for each thermal treatment, following the mixed model: Y = μ + L + S + L × S + E, where μ is the overall mean, L is the random effect of line, S is the fixed effect of sex and E represents the error term. An ANOVA was used to assess the magnitude of mutational variance for DT induced by P-element insertion lines and the control were tested using Dunnett contrasts for each temperature and batch. Those lines that exhibited significant DT differences relative to the control (heterochronic mutants) were considered as lines bearing an insertion in a candidate DT gene.To identify which lines were responsible of the significant line or line-by-sex interaction, phenotypic differences between rG × E < 1, see below) (changes in rank order), the contribution of these two sources of variation was analyzed by means of the equation derived by Robertson [VG × E = [(σE1 - σE2)2 + 2σE1σE2(1 - rG × E)]/2, where VG × E is the G × E variance component, rG × E is the cross-environment genetic correlation and, σE1 and σE2 are the square roots of the among-line variance components of the two thermal environments studied  insertion lines analyzed. VT is presented in angular transformation  and DT expressed in hours. Solid line represents the correlation plot and dashed lines the 95% confidence intervals (r = 0.305).Correlation between viability (VT) and developmental time (DT) for all Click here for fileP[GT1] insertion lines on DT (25°C). Values are shown as the deviation of the insertion line mean from contemporaneous control for each sex separately.Mean values of significant Click here for fileP[GT1] insertion sites, cytologial positions, biological process gene-ontologies and pleiotropy.Genetic information of candidate DT genes including gene names, Click here for file


Utilising data derived from twins and their families, different approaches can be applied to study genetic and environmental influences on human dental variation. The different methods have advantages and limitations and special features of the twinning process are important to consider. Model-fitting approaches have shown that different combinations of additive genetic variance (A), non-additive genetic variance (D), common environmental variance (C), and unique environmental variance (E) contribute to phenotypic variation within the dentition, reflecting different ontogenetic and phylogenetic influences. Epigenetic factors are also proposed as important in explaining differences in the dentitions of monozygotic co-twins. Heritability estimates are high for most tooth size variables, for Carabelli trait and for dental arch dimensions, moderate for intercuspal distances, and low for some occlusal traits. In addition to estimating the contributions of unmeasured genetic and environmental influences to phenotypic variation, structural equation models can also be used to test the effects of measured genetic and environmental factors. Whole-genome linkage analysis, association analysis of putative candidate genes, and whole genome association approaches, now offer exciting opportunities to locate key genes involved in human dental development. Therefore, by assuming that both types of twins have been sampled from the same gene pool and that similar environmental factors act upon them, one can estimate the relative contributions of genetic and environmental influences to observed variation in different features or traits. The calculation of heritability estimates provides a means of quantifying the extent of the genetic contribution to phenotypic variation, with proportions ranging theoretically from 0 to 1. Various formulae can be utilised to calculate estimates of heritability for both quantitative and categorical data, and their standard errors, although few early twin studies provided such estimates. Two types of heritability can be distinguished: ‘narrow-sense’ heritability refers to the contribution of additive genetic variance to observed phenotypic variance, whereas ‘broad-sense’ heritability refers to the total contribution of genetic factors (additive and non-additive) to the observed variation. Additive effects represent the sum of parental genes influencing the offspring's trait, whereas non-additive effects encompass the effects of genetic dominance and gene–gene interaction.There are several assumptions that underlie the classical twin approach and these were not tested fully in many of the early studies. Furthermore, it has often been overlooked that heritability is a population concept, referring to the proportion of genetic variation within a given population at a particular time. The concept should not be applied to a single individual but, rather, to a group of individuals.Kang et al.2A major issue of concern in many previous studies of twins has been the accuracy of zygosity determination. Although comparisons of physical appearance can provide a reasonably reliable means of determining zygosity, errors can occur and these may influence subsequent analyses. The use of blood groups, as well as serum and enzyme polymorphisms, improved the ability to assign zygosities to twins. More recently, the use of highly polymorphic regions of DNA derived from blood or buccal cells has proved to be accurate and reliable.One of the main criticisms of the classical twin model has been based on the assertion that MZ co-twins are likely to share more similar environments post-natally than DZ co-twins, so greater similarities between them compared with DZ co-twins may partly reflect more similar environments rather than more similar genetic constitutions. While this can be an important issue with some behavioural phenotypes, it is less likely to be a major factor in studies of dental morphology, although nutritional similarities could possibly affect dental development.Another consideration is the possibility of an interaction between genetic and environmental influences. The classical twin model tends to assume that these two influences operate independently, hence the often-used phrase ‘nature versus nurture’. This is seldom the situation and there is frequent interaction between genetic and environmental factors.A further criticism of the classical twin model has been whether it is reasonable to extrapolate the findings from twin studies to a general population containing many singletons, given the special nature of the twinning event, twin pregnancies and births, and the upbringing of twins. The nature of the phenotype under investigation is important when attempting to assess the importance of these factors. However, there are some who question whether this is an appropriate assumption, even for dental variables.3The twinning process itself and the circumstances surrounding the birth of twins and their peri-natal development is special. Twinning has been associated with a high peri-natal mortality rateApart from an apparently higher prevalence of peri-natal mortality and morbidity amongst twins, there is another special feature of the twinning process that frequently has been overlooked. MZ twin pairs most often share a common placenta and chorion (around 60–70%), but there are around 20–30% of MZ co-twins who have separate placentas and chorions. Di-chorionic twins are thought to have separated at an early stage of development, probably in the first 5 days post-conception whereas mono-chorionic twins are thought to have separated at a later stage, around six to 9 days post-conception. In around 30% of mono-chorionic MZ twins, there can be arterio–venous anastomoses that can lead to marked differences in physical development. Few studies of dental features in twins have taken account of chorion type, although Burris and HarrisThe fascinating phenomenon of mirror-imaging, where one member of a twin pair ‘mirrors’ the other for one or more features, is well known to most people. However, most of the studies of mirror-imaging in twins have been retrospective reports based on small sample sizes rather than being well-planned prospective studies. To ensure that findings are not purely due to chance, a suite of study variables needs to be defined, measurements and observations made, error studies performed, and comparisons of the frequencies of mirrored features made between MZ twins, DZ twins and singletons. Given that there is some preliminary evidence that mirror-imaging may be related to the timing of the division and therefore the type of placentation,4Apart from the classical twin model, there are several other research methods that can be used when studying twins. One approach that overcomes the problem of possible confounding effects due to common family environment is to study MZ twins who were separated shortly after birth and subsequently reared in separate homes. Assuming that their adoption placement is not influenced by trait-related environmental factors, the similarity of reared apart MZ co-twins can be attributed to shared genes alone.A further design is the MZ co-twin model, which involves comparison of MZ twins where each member of a pair has been exposed to different environmental effects. For example, MZ co-twins may be treated with different orthodontic appliances and the outcomes compared, or the severity of periodontal disease might be compared between MZ co-twins where one is a smoker and the other is not. The MZ co-twin model can also be used to make inferences about the relative contributions of genetic and environmental factors to phenotypes for which the twins are discordant. Differences in the number and position of missing teeth between MZ co-twins raise the likelihood that environmental and/or epigenetic influences during development can lead to quite distinct differences in dental development.Another research design involving twins is the so-called twin half-sib model.Another model that can be used is the opposite-sexed DZ model. This approach focuses on male–female twin pairs and tests whether there are differences in mean values and variances for selected features between these twins compared with other twin types and singletons. Since each member of a male–female twin pair may be exposed to elevated levels of hormones from their co-twin in utero, it is possible that this may lead to observable effects post-natally. Indeed, there is some evidence that tooth size is increased in females belonging to opposite-sexed twin pairs compared with females from same-sexed MZ or DZ groups.52) can be derived according to the formula: h2 = 2(rmz − rdz), where rmz and rdz are the values of correlation coefficients between samples of MZ and DZ co-twins for the feature under investigation.There have been two main quantitative genetic approaches used to clarify causes of observed variation in the human dentition: classical correlation analysis and multiple abstract variance analysis.The development of more sophisticated model-fitting methods to analyse twin data made it possible to estimate the strength of genetic and environmental contributions within calculable confidence intervals.Three additional sources of variation and covariation between twins that need to be considered are assortative mating, genotype-by-environment interaction (GxE) and genotype–environment correlation (CorGE). The presence of assortative mating, if unmeasured, will lower the estimated genetic contribution to variation. To test for assortative mating, data from parents of twins are needed, as described previously in the twin half-sib model. It is unlikely that there is strong positive or negative assortative mating with respect to dental features, and the few attempts to explore this source of variation have failed to find evidence for its presence.Computer programs, such as Mx developed by Neale,A summary of findings relating to genetic and environmental contributions to human dental variation based on the Adelaide twin studies is provided in 6Our previous studies support the view that, even though there is a relatively strong genetic basis to missing or extra teeth, the number or position of affected teeth can be influenced by epigenetic factors.We consider that a multifactorial modelGiven that there is a link between the size and shape of teeth, and hypodontia or supernumerary teeth, we propose that there is likely to be a group of genes that exert pleiotropic effects on all of these dental phenotypes, accounting for their observed co-variation. How many genes are involved remains to be determined and it is possible that it may be a relatively small number. Support for this view is provided by Kangas et al.7In addition to estimating the effects of unmeasured genes and environmental influences, structural equation models can also be used to test the effects of measured genetic and environmental factors. Until recently, the analysis of molecular genetic data focussed on two different approaches, namely whole-genome linkage analysis or association analysis of putative candidate genes, or a combination of the two. More recently the Wellcome Trust Case Control Consortium7.1Linkage analysis establishes relationships between the level of similarity in a phenotype in genetically related individuals and their levels of similarity in regions of the genome. If such a relationship can be established with sufficient statistical confidence, then one or more genes in those regions are possibly involved in phenotype similarity among individuals.Linkage analysis depends on the co-segregation of alleles at a trait locus and a marker (i.e. on the same chromosome and thus violating Mendel's law of independent assortment). Markers are most commonly variable number tandem repeats – short segments of DNA with a repeated sequence, also called ‘microsatellites’. If a pair of offspring has received the same haplotype from a parent in a certain region of the genome, the pair is said to share that parent's alleles in that region identical by descent (IBD). Since offspring receive their haplotypes from two parents, the pair can share 0, 1 or 2 alleles IBD at a certain locus in the region. The IBD status is usually estimated for a number of markers with (approximately) known location along the genome  or 8 cM  intervals) and is used as the measure of genetic similarity at the marker. The IBD status at a marker is informative for the IBD status at any other locus on the chromosome as long as the population recombination fraction is less than 0.5. The IBD status at the marker and the locus are then correlated in the population and hence similarity at the marker is informative for similarity at the locus. The informative locus may be a gene or located near a gene. If variation in the gene and variation in the phenotype are related, then variation in the IBD status at the locus and thus also at the marker will be related to variation in phenotype similarity. It is possible to estimate the variance contributed by a genetic marker to a trait using structural equation modelling or other regression-based approaches.7.2Ultimately, we aim to quantify the specific effects of genes in terms of their trait contributions from additivity and dominance. Linkage analysis may identify a narrow chromosomal region (i.e. quantitative trait locus) containing a gene influencing the trait. Genes with putative function (i.e. ‘candidate’ genes) can then be selected to test for their association with the trait. Known as association analysis, regression-based methods can be used to test whether variation within the gene is related to variation within the trait.Traditional association studies such as case–control designs may provide spurious associations, the result of stratified samples.7.3p < 5 × 10−7), almost all of which were supported by prior findings and/or replication studies. A further 58 loci were identified with p values < 10−5.Technological advances have now made association analysis possible on a genome-wide (GWA) level. Single nucleotide polymorphisms  are the preferred markers, with sets of up to 500 K SNPs on a single chip (e.g. affymetrix). The first successfully documented GWA reported the findings of The Wellcome Trust Case Control Consortium Study.Family studies are not preferred for GWA because it requires very large samples for sufficient statistical power. However, family studies offer several advantages: they enable investigation of parent of origin effects, as maternal and paternal genes can be dissociated; gene x environment interaction effects can be studied, for instance, by measuring affected twins with known environmental exposures; and they are free from population stratification bias because the test of association can be tested within families as well as between families.Once causal genetic polymorphisms are identified the next step is to describe gene (and protein) functions and interactions. Such studies will most likely target gene expression, protein analysis and structure .8With recent advances in molecular biology and genome-scanning techniques, innovative approaches involving the study of twins have much to offer in complementing molecular studies and helping to unravel how genes and the environment contribute to both normal and abnormal phenotypic variation. As Martin et al.Although the identification of key genes for dental development in humans will represent a major step forward, merely identifying genes will not explain fully how various dental anomalies arise in individuals. This is where further exploration of epigenetic factors, using the MZ co-twin model, is likely to be a fruitful area for future study. Already researchers are beginning to study epigenetic biomarkers in an attempt to explain the reasons for observed differences between MZ twin pairs.Competing interests: None declared.Funding:: National Health and Medical Research Council of Australia, Australian Dental Research Foundation, Wellcome Trust, UK.